Search results for: generalization rule

Authors: Jamel Slaughter

Abstract:

Father involvement across the development of a child has been linked to children’s psychological adjustment, fewer behavioral problems, and higher educational attainment. Conversely, there is much less research that highlights father involvement in relation to childhood development during early childhood period prior to preschool age (ages 1-3 years). Most research on fathers and child outcomes have been limited by its focus on the stages of adolescence, middle childhood, and infancy. This study examined the influence of father involvement, during the toddler stage, on 5th grade cognitive development, rule-breaking, and behavior outcomes measured by Child Behavior Checklist (CBCL) scores. Using data from the Early Head Start Research and Evaluation (EHSRE) Study, 1996-2010: United States, a total of 3,001 children and families were identified in 17 sites (cities), representing a diverse demographic sample. An independent samples t-test was run to compare cognitive development, aggressive, and rule-breaking behavior mean scores among children who had early continuous father involvement for the first 14 – 36 months to children who did not have early continuous father involvement for the first 14 – 36 months. Multiple linear regression was conducted to determine if continuous, or non-continuous father involvement (14 month-36 months), can be used to predict outcome scores on the Child Behavior Checklist in aggressive behavior, rule-breaking behavior, and cognitive development, at 5th grade. A statistically significant mean difference in cognitive development scores were found for children who had continuous father involvement (M=1.92, SD=2.41, t (1009) =2.81, p =.005, 95% CI=.146 to .828) compared to those who did not (M=2.60, SD=3.06, t (1009) =-2.38, p=.017, 95% CI= -1.08 to -.105). There was also a statistically significant mean difference in rule-breaking behavior scores between children who had early continuous father involvement (M=1.95, SD=2.33, t (1009) = 3.69, p <.001, 95% CI= .287 to .940), compared to those that did not (M=2.87, SD=2.93, t (1009) = -3.49, p =.001, 95% CI= -1.30 to -.364). No statistically significant difference was found in aggressive behavior scores. Multiple linear regression was performed using continuous father involvement to determine which has the largest relationship to rule-breaking behavior and cognitive development based on CBCL scores. Rule-breaking behavior was found to be significant (F (2, 1008) = 8.353, p<.001), with an R2 of .016. Cognitive development was also significant (F (2, 1008) = 4.44, p=.012), with an R2 of .009. Early continuous father involvement was a significant predictor of rule-breaking behavior and cognitive development at middle childhood. Findings suggest early continuous father involvement during the first 14 – 36 months of their children’s life, may lead to lower levels of rule-breaking behaviors and thought problems at 5th grade.

Keywords: cognitive development, early continuous father involvement, middle childhood, rule-breaking behavior

Procedia PDF Downloads 275

Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

Abstract:

This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

Procedia PDF Downloads 146

Authors: Manvar Sagar, Nikul Virpariya

Abstract:

The advancement in data mining techniques plays an important role in many applications. In context of privacy and security issues, the problems caused by association rule mining technique are investigated by many research scholars. It is proved that the misuse of this technique may reveal the database owner’s sensitive and private information to others. Many researchers have put their effort to preserve privacy in Association Rule Mining. Amongst the two basic approaches for privacy preserving data mining, viz. Randomization based and Cryptography based, the later provides high level of privacy but incurs higher computational as well as communication overhead. Hence, it is necessary to explore alternative techniques that improve the over-heads. In this work, we propose an efficient, collusion-resistant cryptography based approach for distributed Association Rule mining using Shamir’s secret sharing scheme. As we show from theoretical and practical analysis, our approach is provably secure and require only one time a trusted third party. We use secret sharing for privately sharing the information and code based identification scheme to add support against malicious adversaries.

Keywords: Privacy, Privacy Preservation in Data Mining (PPDM), horizontally partitioned database, EMHS, MFI, shamir secret sharing

Procedia PDF Downloads 383

Authors: Tarul Garg, P. N. Agrawal

Abstract:

We propose to define the q-analogue of the α-Bernstein Kantorovich operators and then introduce the q-bivariate generalization of these operators to study the approximation of functions of two variables. We obtain the rate of convergence of these bivariate operators by means of the total modulus of continuity, partial modulus of continuity and the Peetre’s K-functional for continuous functions. Further, in order to study the approximation of functions of two variables in a space bigger than the space of continuous functions, i.e. Bögel space; the GBS (Generalized Boolean Sum) of the q-bivariate operators is considered and degree of approximation is discussed for the Bögel continuous and Bögel differentiable functions with the aid of the Lipschitz class and the mixed modulus of smoothness.

Keywords: Bögel continuous, Bögel differentiable, generalized Boolean sum, K-functional, mixed modulus of smoothness

Procedia PDF Downloads 364

Authors: Todd Zhou, Mikhail Yurochkin

Abstract:

Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

Procedia PDF Downloads 106

Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

Abstract:

The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

Procedia PDF Downloads 58

Authors: Waseem Shahzad, Ayesha Tahir Khan, Hamid Hussain Awan

Abstract:

Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced.

Keywords: ant colony optimization, parallel Ant-MinerPB, vertical partitioning, classification rule discovery

Procedia PDF Downloads 273

Authors: Torkan Jabbariraad

Abstract:

This study explores the role of private regulation as a mode of governance that engages non-state actors in establishing and implementing rules or standards for public goods or services. It examines the various forms, functions, and effects of private regulation on the rule of law and human rights and considers the role and duties of public authorities in monitoring and supporting private regulation. It contends that private regulation should be regarded as a beneficial supplement to public regulation rather than a substitute or rival and that public authorities should find a balance between acknowledging the independence and variety of private actors and securing their accountability and legitimacy. It also recommends that applying the principles and values of good governance to private regulation can improve its quality and efficiency. The study relies on various sources and viewpoints from the literature on governance theory, public law, and human rights and suggests further research and discussion on the topic of private regulation and its consequences for society.

Keywords: private regulation, public authority, governance theory, rule of law, human rights

Procedia PDF Downloads 24

Authors: Manduth Ramchander

Abstract:

Evaluating expressions involving multiplication and division can give rise to the fallacy that brackets can be arbitrarily inserted into expressions involving multiplication and division. The aim of this article was to draw upon mathematical theory to prove that brackets cannot be arbitrarily inserted into expressions involving multiplication and division and in particular in expressions where division precedes multiplication. In doing so, it demonstrates that the notion that two different answers are possible, when evaluating expressions involving multiplication and division, is indeed a false one. Searches conducted in a number of scholarly databases unearthed the rules to be applied when removing brackets from expressions, which revealed that consideration needs to be given to sign changes when brackets are removed. The rule pertaining to expressions involving multiplication and division was then extended upon, in its reverse format, to prove that brackets cannot be arbitrarily inserted into expressions involving multiplication and division. The application of the rule demonstrates that an expression involving multiplication and division can have only one correct answer. It is recommended that both the rule and its reverse be included in the curriculum, preferably at the juncture when manipulation with brackets is introduced.

Keywords: brackets, multiplications and division, operations, order

Procedia PDF Downloads 136

Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

Abstract:

Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

Procedia PDF Downloads 269

Authors: Emma Roberts

Abstract:

The Rome II Regulation has established a uniform regime of conflict of law rules across the European Union (except for Denmark) which determines the law applicable in non-contractual obligations disputes. It marks a significant development towards the Europeanization of private international law and aims to provide the most appropriate connecting factors to achieve both legal certainty and justice in individual cases. Many non-contractual obligations are recognised to present such distinct factors that, to achieve these aims, a special rule is provided for determining the applicable law in cases in respect of product liability and environmental torts, for example. Throughout the legislative process, the European Parliament sought to establish a separate rule for road traffic accidents, recognising that these cases too present such novel situations that a blanket application of a lex loci damni approach would not provide an appropriate answer. Such attempts were rejected and, as a result, cases arising out of road traffic accidents are subject to the Regulation’s general lex loci damni rule along with its escape clause and limited exception. This paper offers a critique of the Regulation’s response to cross-border road traffic accident cases. In England and Wales, there have been few cases that have applied the Regulation’s provisions to date, but significantly the majority of such cases are in respect of road traffic accidents. This paper examines the decisions in those cases and challenges the legislators’ decision not to provide a special rule for such incidences. Owing to the diversity in compensation systems globally, applying the Regulation’s general rule to cases of road traffic accidents – given the breadth of matters that are to be subject to the lex cause – cannot ensure an outcome that provides ‘justice in individual cases’ as is assured by the Regulation's recitals. Not only does this paper suggest that the absence of a special rule for road traffic accidents means that the Regulation fails to achieve one of its principal aims, but it further makes out a compelling case for the legislative body of the European Union to implement a corrective instrument.

Keywords: accidents abroad, applicable law, cross-border torts, non-contractual obligations, road traffic accidents

Procedia PDF Downloads 241

Authors: David Gibbs-Kneller

Abstract:

Only parties to a contract can enforce it. This is the privity of the contract. Carriage contracts frequently involve intermediated relationships. While the carrier and cargo-owner will agree on a contract for carriage, there is no privity or consideration between the cargo-owner and third parties. To overcome this, the contract utilizes ‘bailment on terms’ or the rule in Morris. Morris v C W Martin & Sons Ltd is authority for the following: A sub-bailee and bailor may rely on terms of a bailment where the bailor has consented to sub-bailment “on terms”. Bailment on terms can play a significant part in making litigation decisions and determining liability. It is used in standard form contracts and courts have also strived to find consent to bailment on terms in agreements so as to avoid the consequences of privity of contract. However, what this paper exposes is the false legal basis for this model. Lord Denning gave an account adumbrated of the law of bailments to justify the rule in Morris. What Lord Denning was really doing was objecting to the doctrine of privity. To do so, he wrongly asserted there was a lacuna in law that meant third parties could not avail themselves upon terms of a contract. Next, he provided a false analogy between purely contractual rights and possessory liens. Finally, he gave accounts of authorities to say they supported the rule in Morris when they did not. Surprisingly, subsequent case law on the point has not properly engaged with this reasoning. The Pioneer Container held that since the rule in Morris lay in bailments, the decision is not dependent on the doctrine of privity. Yet the basis for this statement was Morris. Once these reasons have been discounted, all bailment on terms rests on is the claim that the law of bailments is an independent source of law. Bailment on terms should not be retained, for it is contrary to established principles in the law of property, tort, and contract. That undermines the certainty of those principles by risking their collapse because there is nothing that keeps bailment on terms within the confines of bailments only. As such, bailment on terms is not good law and should not be used in standard form contracts or by the courts as a means of determining liability. If bailment on terms is a pragmatic rule to retain, it is recommended that rules governing carriage contracts should be amended.

Keywords: bailment, carriage of goods, contract law, privity

Procedia PDF Downloads 163

Authors: C. Cohen, O. Levi

Abstract:

This work introduces a generalization of the classical Tullock’s model of one-stage contests under complete information with multiple unlimited numbers of contestants. In classical Tullock’s model, the contest winner is not necessarily the highest bidder. Instead, the winner is determined according to a draw in which the winning probabilities are the relative contestants’ efforts. The Tullock modeling fits well political contests, in which the winner is not necessarily the highest effort contestant. This work presents a modified model which uses a simple non-discriminating rule, namely, a parameter to influence the total costs planned for an election, for example, the contest designer can control the contestants' efforts. The winner pays a fee, and the losers are reimbursed the same amount. Our proposed model includes a mechanism that controls the efforts exerted and balances competition, creating a tighter, less predictable and more interesting contest. Additionally, the proposed model follows the fairness criterion in the sense that it does not alter the contestants' probabilities of winning compared to the classic Tullock’s model. We provide an analytic solution for the contestant's optimal effort and expected reward.

Keywords: contests, Tullock's model, political elections, control expenses

Procedia PDF Downloads 123

Authors: Idahosa Osagie Ojo

Abstract:

This research is on colonial racism and the Benin bronze artefacts between 1862 and 1960. It analyses the British racial sentiments against the Benin people that heralded colonial rule and how they influenced the perceptions of the artworks during the period. The aim is to contribute to the knowledge of colonial rule in Benin by bringing to the fore its impacts on the perception and interpretation of the Benin bronze artefacts during the period. Primary and secondary sources were utilised and the historical method was adopted. The findings reveal that the first British racial propaganda against the Benin people started in 1862 and that it was consciously orchestrated to manoeuvre public opinion for the ill-conceived colonial project. The research also reveals that the Benin people were not alone in this, as other peoples of Africa that were targeted for British colonial domination suffered the same fate. Findings also show that racial propaganda was actually used to rationalised colonial rule in Benin and that it later influenced the interpretations and perception of the Benin bronze artefacts throughout the colonial period and beyond.

Keywords: Benin, Bronzes, colonial, racism

Procedia PDF Downloads 93

Authors: Yu Lu

Abstract:

Fixed point theory is a basic tool for the study of the existence of Nash equilibria in game theory. This paper presents a significant generalization of the Veinott-Zhou fixed point theorem for increasing correspondences, which serves as an essential framework for investigating the existence of Nash equilibria in supermodular and quasisupermodular games. To establish our proofs, we explore different conceptions of multivalued increasingness and provide comprehensive results concerning the existence of the largest/least fixed point. We provide two distinct approaches to the proof, each offering unique insights and advantages. These advancements not only extend the applicability of the Veinott-Zhou theorem to a broader range of economic scenarios but also enhance the theoretical framework for analyzing equilibrium behavior in complex game-theoretic models. Our findings pave the way for future research in the development of more sophisticated models of economic behavior and strategic interaction.

Keywords: fixed-point, Tarski’s fixed-point theorem, Nash equilibrium, supermodular game

Procedia PDF Downloads 23

Authors: Isabel Mousinho de Figueiredo

Abstract:

Traditionally it was held that the king can do no wrong. History has proved otherwise, and both the rule of law and the open society call for a diversification of checks and balances, including civil liability in tort. Most jurisdictions are right to fear the excessive cost of such liability for the innocent taxpayer. There are notwithstanding extreme instances where refusing compensation is perceived to be outrageous. Many public bodies end up handing out on a voluntary basis, which leaves room to question its legality and merit. Instead, some criteria can shed light on the fairness of an underlying rationale of such compensation and cordon it off within reasonable limits.

Keywords: comparative law, liability of legislators, public bodies, tort law

Procedia PDF Downloads 71

Authors: Reza Moosavi Mohseni, Wenjun Zhang, Jiling Cao

Abstract:

The aim of the present study is to detect the chaotic behavior in monetary economic relevant dynamical system. The study employs three different forms of Taylor rules: current, forward, and backward looking. The result suggests the existence of the chaotic behavior in all three systems. In addition, the results strongly represent that using expectations especially rational expectation hypothesis can increase complexity of the system and leads to more chaotic behavior.

Keywords: taylor rule, monetary system, chaos theory, lyapunov exponent, GMM estimator

Procedia PDF Downloads 495

Authors: Tran Van Long

Abstract:

Beyond trade facilitation, transparency in the WTO legal context is, implicitly and explicitly, aimed at addressing problems in domestic administrative law. Through the lens of global governance, this paper attempts to shed more light on the power of transparency norms enshrined in multilateral trading agreements under the aegis of the WTO. In this global ruled-base system, transparency has become sufficiently powerful to be a multifunctional instrument for promoting rule of law, good governance, and democracy.

Keywords: WTO, transparency, good governance, rule of law, global administrative law.

Procedia PDF Downloads 255

Authors: H.-H. Doh, J.-M. Yu, Y.-J. Kwon, J.-H. Shin, H.-W. Kim, S.-H. Nam, D.-H. Lee

Abstract:

This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i. e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness.

Keywords: flexible job shop scheduling, decision tree, priority rules, case study

Procedia PDF Downloads 330

Authors: Angela U. Makolo

Abstract:

Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

Procedia PDF Downloads 44

Authors: Dwipa Rizki Utama, Hanief Ibrahim

Abstract:

The development of industry retail in Indonesia is very fast, various strategy was undertaken to boost the customer satisfaction and the productivity purchases to boost the profit, one of which is implementing strategies layout. The purpose of this study is to determine the layout of Qita supermarket, a retail industry in Indonesia, in order to improve customer satisfaction and to maximize the rate of products’ sale as a whole, so as the infrequently purchased products will be purchased. This research uses a literature study method, and one of the data mining methods is association rule which applied in market basket analysis. Data were tested amounted 100 from 160 after pre-processing data, so then the distribution department and 26 departments corresponding to the data previous layout will be obtained. From those data, by the association rule method, customer behavior when purchasing items simultaneously can be studied, so then the layout of the supermarket based on customer behavior can be determined. Using the rapid miner software by the minimal support 25% and minimal confidence 30% showed that the 14th department purchased at the same time with department 10, 21st department purchased at the same time with department 13, 15th department purchased at the same time with department 12, 14th department purchased at the same time with department 12, and 10th department purchased at the same time with department 14. From those results, a better supermarket layout can be arranged than the previous layout.

Keywords: industry retail, strategy, association rule, supermarket

Procedia PDF Downloads 167

Authors: Z. Gharaee

Abstract:

Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

Procedia PDF Downloads 138

Authors: Amdouni Hamida, Gammoudi Mohamed Mohsen

Abstract:

In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.

Keywords: association rules, rule-based classification, classification quality, validation

Procedia PDF Downloads 415

Authors: Nousheen Hashmi, Shoab Ahmad Khan

Abstract:

Active collaboration among the green energy sources and the load demand leads to serious issues related to power quality and stability. The growing number of green energy resources and Distributed-Generators need newer strategies to be incorporated for their operations to keep the power energy stability among green energy resources and micro-grid/Utility Grid. This paper presents a novel technique for energy power management in Grid-Connected Photovoltaic with energy storage system under set of constraints including weather conditions, Load Shedding Hours, Peak pricing Hours by using rule-based fuzzy smart grid controller to schedule power coming from multiple Power sources (photovoltaic, grid, battery) under the above set of constraints. The technique fuzzifies all the inputs and establishes fuzzify rule set from fuzzy outputs before defuzzification. Simulations are run for 24 hours period and rule base power scheduler is developed. The proposed fuzzy controller control strategy is able to sense the continuous fluctuations in Photovoltaic power generation, Load Demands, Grid (load Shedding patterns) and Battery State of Charge in order to make correct and quick decisions.The suggested Fuzzy Rule-based scheduler can operate well with vague inputs thus doesn’t not require any exact numerical model and can handle nonlinearity. This technique provides a framework for the extension to handle multiple special cases for optimized working of the system.

Keywords: photovoltaic, power, fuzzy logic, distributed generators, state of charge, load shedding, membership functions

Procedia PDF Downloads 462

Authors: Axel Prettl

Abstract:

This paper provides an overview of the different CFC rule settings in the OECD and 22 additional countries for the years 2004 to 2014 and compares them. In order to do so, it gives a summary of law amendments for every country, provides a comparison and afterwards all CFC rules are rated in their ”power of anti-avoidance” over time. For that rating of CFC rules, the largest common denominator of rule characteristics is used to keep it as abstract as necessary and possible. The paper points out that the CFC rules in the considered countries are very different in their specifications and they reach from very strict to very low binding. All in all these rules get more and more common and important; more countries implement a CFC legislation and the strictness of most of them rises over time.

Keywords: CFC rules, international taxation, corporate taxation, country comparison

Procedia PDF Downloads 288

Authors: Nana Adjoah Mbroh, Suares Clovis Oukouomi Noutchie

Abstract:

Singularly perturbed problems are parameter dependent problems, and they play major roles in the modelling of real-life situational problems in applied sciences. Thus, designing efficient numerical schemes to solve these problems is of much interest since the exact solutions of such problems may not even exist. Generally, singularly perturbed problems are identified by a small parameter multiplying at least the highest derivative in the equation. The presence of this parameter causes the solution of these problems to be characterized by rapid oscillations. This unique feature renders classical numerical schemes inefficient since they are unable to capture the behaviour of the exact solution in the part of the domain where the rapid oscillations are present. In this paper, a numerical scheme is proposed to solve a singularly perturbed Volterra Integro-differential equation. The scheme is based on the midpoint rule and employs the non-standard finite difference scheme to solve the differential part whilst the composite trapezoidal rule is used for the integral part. A fully fledged error estimate is performed, and Richardson extrapolation is applied to accelerate the convergence of the scheme. Numerical simulations are conducted to confirm the theoretical findings before and after extrapolation.

Keywords: midpoint rule, non-standard finite difference schemes, Richardson extrapolation, singularly perturbed problems, trapezoidal rule, uniform convergence

Procedia PDF Downloads 108

Authors: Adeola Ayodele Oluwabiyi

Abstract:

The study undertook an in depth examination of the relevant sections of the Nigerian Constitution and the Central Bank of Nigeria (CBN) Act as it relates to the appointment and removal of the CBN Governor; It analysed the Constitutional issues that arose from the removal of the immediate past Governor of the CBN; and made recommendations as appropriate. The study relied on primary and secondary sources of information. The primary sources included the Constitution of the Federal Republic of Nigeria, Statutes, Conventions and Judicial decisions, while the secondary sources included Books, Journals Articles, Newspapers and Internet Materials. The study revealed that the removal of the CBN Governor was not in accordance with the Nigerian Constitution and the CBN Act that Guarantee such. It also revealed some of the arguments in support of the removal. The study concluded that the removal of the immediate past Governor of CBN was an outright disregard for the rule of law. The study concluded that if Government treat the laws in question with levity and contempt the confidence of the citizens in such government will be seriously eroded and the effect of that will be the beginning of anarchy in replacement of the rule of law. It could also have serious economic implications on the economy of any nation.

Keywords: central bank, governor, laws, Nigeria

Procedia PDF Downloads 373

Authors: P. Bharathiraja

Abstract:

Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

Procedia PDF Downloads 494

Authors: Yehjune Heo

Abstract:

As biometric systems become widely deployed, the security of identification systems can be easily attacked by various spoof materials. This paper contributes to finding a reliable and practical anti-spoofing method using Convolutional Neural Networks (CNNs) based on the types of loss functions and optimizers. The types of CNNs used in this paper include AlexNet, VGGNet, and ResNet. By using various loss functions including Cross-Entropy, Center Loss, Cosine Proximity, and Hinge Loss, and various loss optimizers which include Adam, SGD, RMSProp, Adadelta, Adagrad, and Nadam, we obtained significant performance changes. We realize that choosing the correct loss function for each model is crucial since different loss functions lead to different errors on the same evaluation. By using a subset of the Livdet 2017 database, we validate our approach to compare the generalization power. It is important to note that we use a subset of LiveDet and the database is the same across all training and testing for each model. This way, we can compare the performance, in terms of generalization, for the unseen data across all different models. The best CNN (AlexNet) with the appropriate loss function and optimizers result in more than 3% of performance gain over the other CNN models with the default loss function and optimizer. In addition to the highest generalization performance, this paper also contains the models with high accuracy associated with parameters and mean average error rates to find the model that consumes the least memory and computation time for training and testing. Although AlexNet has less complexity over other CNN models, it is proven to be very efficient. For practical anti-spoofing systems, the deployed version should use a small amount of memory and should run very fast with high anti-spoofing performance. For our deployed version on smartphones, additional processing steps, such as quantization and pruning algorithms, have been applied in our final model.

Keywords: anti-spoofing, CNN, fingerprint recognition, loss function, optimizer

Procedia PDF Downloads 113

Authors: Rihab Idoudi, Karim Saheb Ettabaa, Basel Solaiman, Kamel Hamrouni

Abstract:

Medical association rules induction is used to discover useful correlations between pertinent concepts from large medical databases. Nevertheless, ARs algorithms produce huge amount of delivered rules and do not guarantee the usefulness and interestingness of the generated knowledge. To overcome this drawback, we propose an ontology based interestingness measure for ARs ranking. According to domain expert, the goal of the use of ARs is to discover implicit relationships between items of different categories such as ‘clinical features and disorders’, ‘clinical features and radiological observations’, etc. That’s to say, the itemsets which are composed of ‘similar’ items are uninteresting. Therefore, the dissimilarity between the rule’s items can be used to judge the interestingness of association rules; the more different are the items, the more interesting the rule is. In this paper, we design a distinct approach for ranking semantically interesting association rules involving the use of an ontology knowledge mining approach. The basic idea is to organize the ontology’s concepts into a hierarchical structure of conceptual clusters of targeted subjects, where each cluster encapsulates ‘similar’ concepts suggesting a specific category of the domain knowledge. The interestingness of association rules is, then, defined as the dissimilarity between corresponding clusters. That is to say, the further are the clusters of the items in the AR, the more interesting the rule is. We apply the method in our domain of interest – mammographic domain- using an existing mammographic ontology called Mammo with the goal of deriving interesting rules from past experiences, to discover implicit relationships between concepts modeling the domain.

Keywords: association rule, conceptual clusters, interestingness measures, ontology knowledge mining, ranking

Procedia PDF Downloads 302